An AI-powered patient triage platform for future viral outbreaks using COVID-19 as a disease model

Over the last century, outbreaks and pandemics have occurred with disturbing regularity, necessitating advance preparation and large-scale, coordinated response. Here, we developed a machine learning predictive model of disease severity and length of hospitalization for COVID-19, which can be utilized as a platform for future unknown viral outbreaks. We combined untargeted metabolomics on plasma data obtained from COVID-19 patients (n = 111) during hospitalization and healthy controls (n = 342), clinical and comorbidity data (n = 508) to build this patient triage platform, which consists of three parts: (i) the clinical decision tree, which amongst other biomarkers showed that patients with increased eosinophils have worse disease prognosis and can serve as a new potential biomarker with high accuracy (AUC = 0.974), (ii) the estimation of patient hospitalization length with ± 5 days error (R2 = 0.9765) and (iii) the prediction of the disease severity and the need of patient transfer to the intensive care unit. We report a significant decrease in serotonin levels in patients who needed positive airway pressure oxygen and/or were intubated. Furthermore, 5-hydroxy tryptophan, allantoin, and glucuronic acid metabolites were increased in COVID-19 patients and collectively they can serve as biomarkers to predict disease progression. The ability to quickly identify which patients will develop life-threatening illness would allow the efficient allocation of medical resources and implementation of the most effective medical interventions. We would advocate that the same approach could be utilized in future viral outbreaks to help hospitals triage patients more effectively and improve patient outcomes while optimizing healthcare resources

Hypertrophy of mature xenopus muscle fibres in culture induced by synergy of albumin and insulin

The aim of this study was to investigate effects of albumin and insulin separately as well as in combination on mature muscle fibres during long-term culture. Single muscle fibres were dissected from m. iliofibularis of Xenopus laevis and attached to a force transducer in a culture chamber. Fibres were cultured in a serum-free medium at slack length (mean sarcomere length 2.3 μm) for 8 to 22 days. The medium was supplemented with (final concentrations): (1) bovine insulin (6 nmol/L or 200-600 nmol/L), (2) 0.2% bovine albumin or (3) 0.2% bovine albumin in combination with insulin (120 nmol/L). In culture medium with insulin, 50% of the muscle fibres became in-excitable within 7-12 days, whereas the other 50% were stable. Caffeine contractures of in-excitable muscle fibres produced 80.4±2.4% of initial peak tetanic force, indicating impaired excitation-contraction (E-C) coupling in in-excitable fibres. In the presence of albumin, all cultured muscle fibres were stable for at least 10 days. Muscle fibres cultured in medium with insulin or albumin exclusively did not hypertrophy or change the number of sarcomeres in series. In contrast, muscle fibres cultured with both albumin and insulin showed an increase in tetanic force and fibre cross-sectional area of 19.6±2.8% and 32.5±4.9%, respectively, (means±SEM.; P=0.007) after 16.3±1.7 days, whereas the number of sarcomeres in series remained unchanged. We conclude that albumin prevents muscle fibre damage and preserves E-C coupling in culture. Furthermore, albumin is important in regulating muscle fibre adaptation by a synergistic action with growth factors like insulin. © 2008 The Author(s)

The role of protected areas in the avoidance of anthropogenic conversion in a high pressure region : a matching method analysis in the core region of the brazilian cerrado

Global efforts to avoid anthropogenic conversion of natural habitat rely heavily on the establishment of protected areas. Studies that evaluate the effectiveness of these areas with a focus on preserving the natural habitat define effectiveness as a measure of the influence of protected areas on total avoided conversion. Changes in the estimated effectiveness are related to local and regional differences, evaluation methods, restriction categories that include the protected areas, and other characteristics. The overall objective of this study was to evaluate the effectiveness of protected areas to prevent the advance of the conversion of natural areas in the core region of the Brazil’s Cerrado Biome, taking into account the influence of the restriction degree, governmental sphere, time since the establishment of the protected area units, and the size of the area on the performance of protected areas. The evaluation was conducted using matching methods and took into account the following two fundamental issues: control of statistical biases caused by the influence of covariates on the likelihood of anthropogenic conversion and the non-randomness of the allocation of protected areas throughout the territory (spatial correlation effect) and the control of statistical bias caused by the influence of auto-correlation and leakage effect. Using a sample design that is not based on ways to control these biases may result in outcomes that underestimate or overestimate the effectiveness of those units. The matching method accounted for a bias reduction in 94–99% of the estimation of the average effect of protected areas on anthropogenic conversion and allowed us to obtain results with a reduced influence of the auto-correlation and leakage effects. Most protected areas had a positive influence on the maintenance of natural habitats, although wide variation in this effectiveness was dependent on the type, restriction, governmental sphere, size and age group of the unit

Provenancing Archaeological Wool Textiles from Medieval Northern Europe by Light Stable Isotope Analysis (δ13C, δ15N, δ2H)

We investigate the origin of archaeological wool textiles preserved by anoxic waterlogging from seven medieval archaeological deposits in north-western Europe (c. 700-1600 AD), using geospatial patterning in carbon (δ13C), nitrogen (δ15N) and non-exchangeable hydrogen (δ2H) composition of modern and ancient sheep proteins. δ13C, δ15N and δ2H values from archaeological wool keratin (n = 83) and bone collagen (n = 59) from four sites were interpreted with reference to the composition of modern sheep wool from the same regions. The isotopic composition of wool and bone collagen samples clustered strongly by settlement; inter-regional relationships were largely parallel in modern and ancient samples, though landscape change was also significant. Degradation in archaeological wool samples, examined by elemental and amino acid composition, was greater in samples from Iceland (Reykholt) than in samples from north-east England (York, Newcastle) or northern Germany (Hessens). A nominal assignment approach was used to classify textiles into local/non-local at each site, based on maximal estimates of isotopic variability in modern sheep wool. Light element stable isotope analysis provided new insights into the origins of wool textiles, and demonstrates that isotopic provenancing of keratin preserved in anoxic waterlogged contexts is feasible. We also demonstrate the utility of δ2H analysis to understand the location of origin of archaeological protein samples

Leptospira spp. Toolbox for Chemotaxis Assay

International audienceAbstract We developed an invasion assay by using microfabricated culture devices. First, ovarian tumor spheroids were generated with a culture patch device consisting of an agarose membrane formed with a honeycomb microframe – the patch – and gelatin nanofiber backbone. By changing the dimensions of the honeycomb compartments we were able to control the number of cells and size of the spheroids. When the spheroids were placed on a patch coated with a thin membrane of fibrillary type I collagen, spheroid disruption was observed due to substrate induced cell migration. This process is straightforward and should be applicable to other cancer types, as well as assays under microfluidic conditions, thereby holding the potential for use in tumor modeling and anti-cancer drug development